CAJ | 학술논문

为准确、快速地预测回采工作面瓦斯涌出量，提出一种基于主成分分析法（PCA）和改进的果蝇算法（MFOA）优化支持向量机（SVM）的回采工作面绝对瓦斯涌出量预测模型。模型首先运用PCA方法对原始数据进行降维处理，消除数据冗余，而后采用改进的果蝇算法对SVM参数进行全局寻优，避免SVM参数的选取对模型预测结果的不利影响，最终建立基于PCA-MFOA-SVM的耦合预测模型，并以实际监测数据为例进行仿真预测。结果表明：该模型预测的平均绝对误差为0.0775 m3/t，平均相对误差为1.3237%，与其他模型相比，预测精度高，综合性能好，能够实现回采工作面瓦斯涌出量的动态预测。
위준학、쾌속지예측회채공작면와사용출량，제출일충기우주성분분석법（PCA）화개진적과승산법（MFOA）우화지지향량궤（SVM）적회채공작면절대와사용출량예측모형。모형수선운용PCA방법대원시수거진행강유처리，소제수거용여，이후채용개진적과승산법대SVM삼수진행전국심우，피면SVM삼수적선취대모형예측결과적불리영향，최종건립기우PCA-MFOA-SVM적우합예측모형，병이실제감측수거위례진행방진예측。결과표명：해모형예측적평균절대오차위0.0775 m3/t，평균상대오차위1.3237%，여기타모형상비，예측정도고，종합성능호，능구실현회채공작면와사용출량적동태예측。
In order to predict the gas emission of working face accurately and quickly, a prediction model for gas emission of working face is put forward based on Principal Component Analysis(PCA), Modified Fruit Fly Optimization Algorithm (MFOA)and optimized Support Vector Machine(SVM). PCA is used for dimensionality reduction of original data, elimi-nating data redundancy, MFOA is used to optimize SVM parameters, avoids the negative impact of the model prediction results affected by selection of SVM parameters. Eventually, coupling prediction model is established based on PCA-MFOA-SVM, and simulation forecast is done as example by taking actual monitoring data. Results show that the mean absolute error of this model prediction is 0.077 5 m3/t, the mean relative error is 1.323 7%. Comparing with other models this model can realize the dynamic prediction of gas emission in the working face with its higher prediction accuracy and better comprehensive performance.